TY - GEN
T1 - Learning From Neural Control
AU - Wang, Cong
AU - Hill, David J.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2003
Y1 - 2003
N2 - One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, we firstly present an adaptive neural controller which is capable of learning the system dynamics during tracking control to periodic reference orbits. A partial persistent excitation (PE) condition is shown to be satisfied, and accurate NN approximation for the unknown dynamics is obtained in a local region along the tracking orbit. Secondly, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve local stability and better control performance. The significance of this paper is that it presents a dynamical deterministic learning theory, which can implement learning and control abilities similarly to biological systems.
AB - One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, we firstly present an adaptive neural controller which is capable of learning the system dynamics during tracking control to periodic reference orbits. A partial persistent excitation (PE) condition is shown to be satisfied, and accurate NN approximation for the unknown dynamics is obtained in a local region along the tracking orbit. Secondly, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve local stability and better control performance. The significance of this paper is that it presents a dynamical deterministic learning theory, which can implement learning and control abilities similarly to biological systems.
UR - https://www.scopus.com/pages/publications/1542378336
UR - https://www.scopus.com/pages/publications/1542378336#tab=impact
U2 - 10.1109/CDC.2003.1271916
DO - 10.1109/CDC.2003.1271916
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780379241
VL - 6
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5721
EP - 5726
BT - 42nd IEEE International Conference on Decision and Control, CDC 2003 - Proceedings
PB - IEEE
T2 - 42nd IEEE International Conference on Decision and Control, CDC 2003
Y2 - 9 December 2003 through 12 December 2003
ER -